Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

19 papers of 11,817Sort Recent · Most cited
  1. 2026
    Intrinsic gradient oxygen-driven second-order memristors for continual reinforcement learningJianyu Ming, Ruiheng Wang, Jingwei Fu … Wei HuangNature Communications
  2. 2025
    Bayesian continual learning and forgetting in neural networksDjohan Bonnet, K. Cottart, T. Hirtzlin … D. QuerliozNature Communications
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  3. 2025
    Electrochemical ohmic memristors for continual learningShaochuan Chen, Zhen Yang, Heinrich Hartmann … I. ValovNature Communications
  4. 2025
    Hybrid neural networks for continual learning inspired by corticohippocampal circuitsQianqian Shi, Faqiang Liu, Hongyi Li … Rong ZhaoNature Communications
  5. 2025
    Layer ensemble averaging for fault tolerance in memristive neural networksOsama Yousuf, Brian D. Hoskins, Karthick Ramu … Gina C. AdamNature Communications
  6. 2024
    Rapid context inference in a thalamocortical model using recurrent neural networksWeilong Zheng, Zhongxuan Wu, Ali Hummos … Michael M. HalassaNature Communications
  7. 2024
    Self-Contrastive Forward-Forward algorithmXing Chen, Dong-Shu Liu, Jérémie Laydevant, J. GrollierNature Communications
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  8. 2023
  9. 2023
    A sparse quantized hopfield network for online-continual memoryN. Alonso, J. KrichmarNature Communications
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  10. 2022
    Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networksTimothy Tadros, Giri P. Krishnan, Ramyaa Ramyaa, Maxim BazhenovNature Communications · University of California San Diego · New Mexico Institute of Mining and Technology
  11. 2022
    Experimentally validated memristive memory augmented neural network with efficient hashing and similarity searchRuibin Mao, Bo Wen, Arman Kazemi … Can LiNature Communications · University of Hong Kong · University of Notre Dame · +5
  12. 2022
    A framework for the general design and computation of hybrid neural networksRong Zhao, Zheyu Yang, Hao Zheng … Luping ShiNature Communications · Chinese Institute for Brain Research · Tsinghua University · +1
  13. 2022
    EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle pickingXinyu Zhang, Tian-Fang Zhao, Jiansheng Chen … Xueming LiNature Communications · Tsinghua University · University of Science and Technology Beijing · +2
  14. 2022
    Introducing principles of synaptic integration in the optimization of deep neural networksGiorgia Dellaferrera, Stanisław Woźniak, Giacomo Indiveri … Evangelos EleftheriouNature Communications · University of Zurich · IBM Research - Zurich · +3
  15. 2020
    Brain-inspired global-local learning incorporated with neuromorphic computingYujie Wu, Rong Zhao, Jun Zhu … Luping ShiNature Communications · Tsinghua University · Chinese Institute for Brain Research · +1
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  16. 2020
    Synaptic metaplasticity in binarized neural networksAxel Laborieux, Maxence Ernoult, Tifenn Hirtzlin, Damien QuerliozNature Communications · Centre National de la Recherche Scientifique · Université Paris-Saclay · +2
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  17. 2020
    Brain-inspired replay for continual learning with artificial neural networksGido M. van de Ven, Hava T. Siegelmann, Andreas S. ToliasNature Communications · Baylor College of Medicine · University of Cambridge · +3
  18. 2017
    Habituation based synaptic plasticity and organismic learning in a quantum perovskiteFan Zuo, Priyadarshini Panda, Michele Kotiuga … Shriram RamanathanNature Communications · Purdue University West Lafayette · Rutgers, The State University of New Jersey · +3
  19. 2017
    Reminders of past choices bias decisions for reward in humansAaron M. Bornstein, Mel Win Khaw, Daphna Shohamy, Nathaniel D. DawNature Communications · Princeton University · Columbia University · +2
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.